How we learn: why brains learn better than any machine ... for now
(Book)

Book Cover
Uniform Title:
Published:
[New York, New York] : Viking, [2020].
Format:
Book
Physical Desc:
## pages, ## unnumbered pages of plates : illustrations (some color), color maps ; ## cm, xxviii, 319 pages, 16 unnumbered pages of plates : illustrations (chiefly color) ; 24 cm
Status:
Copies
Location
Call Number
Status
Last Check-In
Boulder Main Adult NonFiction
153.15 Deha
Due May 12, 2024
Description

"In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age. We can all "learn to learn" by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback, and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. The exciting advancements in A.I. of the last twenty years reveal just as much about our remarkable abilities as they do about the potential of machines. How We Learn finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain's learning algorithms, in our schools and universities as well as in everyday life"--

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Language:
English
ISBN:
9780525559887 (hardcover), 0525559884 (hardcover)

Notes

General Note
"Based on, in part, Apprendre!, published in France by Odile Jacob, Paris, in 2018"--Title page verso.
Bibliography
Includes bibliographical references and index.
Description
"In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age. We can all "learn to learn" by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback, and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. The exciting advancements in A.I. of the last twenty years reveal just as much about our remarkable abilities as they do about the potential of machines. How We Learn finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain's learning algorithms, in our schools and universities as well as in everyday life"--,Provided by publisher.
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Citations
APA Citation (style guide)

Dehaene, S. (2020). How we learn: why brains learn better than any machine ... for now. [New York, New York], Viking.

Chicago / Turabian - Author Date Citation (style guide)

Dehaene, Stanislas. 2020. How We Learn: Why Brains Learn Better Than Any Machine ... for Now. [New York, New York], Viking.

Chicago / Turabian - Humanities Citation (style guide)

Dehaene, Stanislas, How We Learn: Why Brains Learn Better Than Any Machine ... for Now. [New York, New York], Viking, 2020.

MLA Citation (style guide)

Dehaene, Stanislas. How We Learn: Why Brains Learn Better Than Any Machine ... for Now. [New York, New York], Viking, 2020.

Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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Grouped Work ID:
758474e9-b3f8-6b19-427f-cbe9e7eab246
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Record Information

Last Sierra Extract TimeApr 21, 2024 01:13:55 PM
Last File Modification TimeApr 21, 2024 01:14:04 PM
Last Grouped Work Modification TimeApr 21, 2024 01:13:58 PM

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5050 |a Seven definitions of learning -- Why our brain learns better than current machines -- Babies' invisible knowledge -- The birth of a brain -- Nurture's share -- Recycle your brain -- Attention -- Active engagement -- Error feedback -- Consolidation -- Conclusion. Reconciling education with neuroscience.
520 |a "In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age. We can all "learn to learn" by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback, and consolidation. The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled, and it remains the best source of inspiration for recent developments in artificial intelligence. The exciting advancements in A.I. of the last twenty years reveal just as much about our remarkable abilities as they do about the potential of machines. How We Learn finds the boundary of computer science, neurobiology, and cognitive psychology to explain how learning really works and how to make the best use of the brain's learning algorithms, in our schools and universities as well as in everyday life"--|c Provided by publisher.
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